#Google FONTS
library(showtext)
#Lato for numbers, Assistant for text
font_add_google("Lato")
font_add_google("Assistant")
showtext_auto()
# Read in all three datasets.
life_expectancy_years <- read_csv("C:/Users/Adam Moffitt/OneDrive - West Chester University of PA/STA553/Week 5/life_expectancy_years.csv")
## Rows: 187 Columns: 220
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): geo
## dbl (219): 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810,...
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
income_per_person <- read_csv("C:/Users/Adam Moffitt/OneDrive - West Chester University of PA/STA553/Week 5/income_per_person.csv")
## Rows: 193 Columns: 220
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): geo
## dbl (219): 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810,...
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
countries_total <- read_csv("C:/Users/Adam Moffitt/OneDrive - West Chester University of PA/STA553/Week 5/countries_total.csv")
## Rows: 248 Columns: 11
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (7): name, alpha-2, alpha-3, iso_3166-2, region, sub-region, intermediat...
## dbl (4): country-code, region-code, sub-region-code, intermediate-region-code
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
population <- read_csv("C:/Users/Adam Moffitt/OneDrive - West Chester University of PA/STA553/Week 5/population_total.csv")
## Rows: 195 Columns: 220
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): geo
## dbl (219): 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810,...
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Manipulated the data to write a final data set
lifeexpectancylong <- life_expectancy_years %>%
gather(key = "Year", # the column names of the wide table
value = "LifeExp", # the numerical values of the table
- geo, # drop geo variable: its value will not be gathered (stacked)!
na.rm = TRUE
)
incomeperpersonlong <- income_per_person %>%
gather(key = "Year", # the column names of the wide table
value = "Income", # the numerical values of the table
- geo, # drop geo variable: its value will not be gathered (stacked)!
na.rm = TRUE
) # removing records with missing values ?
# combine the two tables together
populationlong <- population %>%
gather(key = "Year", # the column names of the wide table
value = "Population", # the numerical values of the table
- geo, # drop geo variable: its value will not be gathered (stacked)!
na.rm = TRUE
)
LifeExpIncom <-left_join(incomeperpersonlong , lifeexpectancylong, by = c("geo" , "Year"))
LifeExpIncom2 <- left_join(LifeExpIncom , populationlong , by = c("geo","Year"))
## Subregions data set
subregions <- countries_total %>%
select(name , region)
# Add sub regions to create final data set.
LifeExpIncomFinal <- LifeExpIncom2 %>% left_join(subregions, by = c("geo" = "name"))
## New names:
## * `` -> ...1
## * ...1 -> ...2
## * ...2 -> ...3
## * ...3 -> ...4
## * ...4 -> ...5
## Rows: 42486 Columns: 11
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (2): geo, region
## dbl (9): ...1, ...2, ...3, ...4, ...5, Year, Income, LifeExp, Population
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
#Location of Final dataset on Github
LifeExpIncomFinal <- "https://raw.githubusercontent.com/APM3030/STA553/main/homework3/LifeExpIncomFinal.csv"
LifeExpIncomFinal <- read_csv(LifeExpIncomFinal)
## New names:
## * `` -> ...1
## * ...1 -> ...2
## Rows: 42486 Columns: 8
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (2): geo, region
## dbl (6): ...1, ...2, Year, Income, LifeExp, Population
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
#convert year to integer
LifeExpIncomFinal <- mutate(LifeExpIncomFinal, Year = as.integer(LifeExpIncomFinal$Year))
ggplot(LifeExpIncomFinal, aes(Income, LifeExp,
size = Population, colour = region)) +
geom_point(alpha = 0.5) +
guides(size = FALSE)+
scale_size(range = c(2, 12)) +
scale_x_log10(labels = scales::dollar) +
labs(title = 'Relationship Between GDP and Life Expectancy' ,
subtitle = 'Year: {frame_time}',
x = 'GDP Per Capita',
y = 'Life Expectancy in Years',
colour = 'Region:')+
theme_minimal() +
theme(
axis.line = element_line(size = .7, linetype = "solid") ,
text=element_text(size = 18 , family="Assistant"),
axis.title.y = element_text(vjust = -2),
axis.title.x = element_text(vjust = 3),
plot.title = element_text(face = "bold" , size = 26 , vjust = -3.5),
plot.subtitle = element_text(vjust = -1),
axis.text.x = element_text(family = "Lato" , size = 16),
axis.text.y = element_text(family = "Lato" , size = 16),
legend.title = element_text(face = "bold")
) +
transition_time(Year)
#subet data for year 2015
leifinal2015 <- LifeExpIncomFinal %>% filter(Year == 2015)
w <-
ggplot(leifinal2015, aes(Income, LifeExp,
size = Population, colour = geo)) +
geom_point(alpha = 0.5) +
scale_size(range = c(2, 12)) +
scale_x_log10(labels = scales::dollar) +
labs(title = 'Relationship Between GDP and Life Expectancy' ,
subtitle = 'Year: 2015',
x = 'GDP Per Capita',
y = 'Life Expectancy in Years',
)+
theme_minimal() +
theme(
axis.line = element_line(size = .7, linetype = "solid") ,
plot.title = element_text(face = "bold"),
legend.position = "none"
)
ggplotly(w)